He wasn't taping yet.
OK, so thank you for coming.
My pleasure.
So why don't you tell me what you're doing right now?
What I'm doing right now in the field of AI
is concerned with cases and examples
and how one reasons with them.
Particularly interested in the problem of how
one reasons with concepts that are messy in the sense
that they're not well-defined say by logical definition
to necessary intuition conditions,
concepts that have exceptions and holes in them,
and concepts that change, and the role
of examples in cases in representing
those sorts of things.
OK.
That's what drives me.
I love examples and cases and hypotheticals.
Fabulous, and how did you get into the field originally?
Well, came up pretty classically through the mathematics route.
I was an undergraduate mathematics major at Brown
and I loved mathematics.
I also like thinking about the problem of how
you get good at mathematics but nonetheless, eventually ended
up in the MIT math department as a regular graduate student.
And at some point got sort of bitchy
about continuing to be a graduate student.
And took a year off to go work at Lincoln Labs supposedly
to get some experience on stability problems etc.
but when I got to the labs I ended up being put on a project
that I found quite boring.
And I resorted to the sorts of things
I did high school which was go to the library
and think about things I wanted to think about.
How terrible.
And I proposed a project that would essentially
build what we would call today Mathematicians Workbench, that
would be a repository for information like theorems
and definitions and counterexamples in mathematics
and all the interconnections amongst them
so that you could ask questions.
Well, if I'm looking at this theorem can
you tell me whether I can drop a precondition
and if so, point me to the theorem and the proof, if not
show me the counterexample.
Is this a program?
Oh by the way--
I wanted it-- it's essentially a hypertext web-like environment
that would let you navigate very, very
freely among this highly interconnected knowledge
base of mathematics.
And so I wanted to first decide what should be in the knowledge
base and then build a program that would let you
you know, swing around it very freely.
And that's how it started off and I proposed that
to my supervisors at MIT--
at Lincoln Labs.
And they said, well no, we're not funded to let you do that.
But back on campus some people in the math department,
particularly they chair, heard about it
and said, why don't you go talk to Marvin and Seymour
about this idea.
And Seymour in particular who's a professor,
Seymour Papert who's a professor of mathematics
said, well, why don't you come back to do this as your thesis?
What year was this?
This was-- I was at Lincoln Labs in '74-'75 and so I was having
these conversations with them I guess the summer
of '74, the fall of '74.
I'd have to check my dates exactly.
Were they both the-- heading the AI lab at the time?
Yes, but I didn't know them so much through the AI lab
as through my home base in the math department.
OK.
There's an awful lot of AI that has happened
in the math department at MIT.
There's a really strong tradition.
And so-- and of the mathematicians
there are not scared to think about things in new ways
and so in fact the person who probably pointed me off
in this direction is a classical functional analyst type.
And so, so anyway, Seymour said, why don't you
come back to campus and do this as your thesis?
And whereas I thought I was going to do
a thesis on something else.
And I thought gee, my hobby is my thesis,
what could be better?
And so it's essentially a swing over to the AI side which is--
now you would call it sort of an [INAUDIBLE] representation
or an environmental kind of thing.
And one of the important things I thought
to capture in this environment were the examples,
how one counterexample or one example is build from another.
And I thought that was as much an ingredient
of mathematical knowledge as the theorems,
how one theorem is proved from another theorem.
And of course there's interconnections
between the theorems and the examples
so my initial work was on building up
this representation that made room
for these sort of non-logical or non-Sunday school mathematics
kinds of objects.
And so--
Had any other stuff been done like that before?
No, I mean, there certainly were compendia of counterexamples
in mathematics.
There certainly were books of theorems,
I mean obviously classical mathematics is all about that.
And there were a few instances of how
to think about mathematics such as Polya.
But a representational scheme in an environment like this, no.
And so there was the representational substrate
and epistemology of mathematics if you want.
A way to use that representation for the mathematician
who knows what she wants.
And then I had this idea that with all the structure
you should be able to describe what
it means to understand the body of mathematics well,
and then to help someone else learn how to understand
a body of mathematics.
And this is more Polya-esque in the sense
that if someone-- if you learn a theorem you should ask well
is the converse true?
And if it's not true well, what's the counterexample that
disproves it?
You should learn to ask those kinds of questions
to weave together this landscape of mathematical knowledge
and to know your way around it and know what's important
and know what's just merely a stepping stone
and know how certain examples can tie together
different concepts.
How some ideas permeate things, the heuristic
content of mathematics.
So for instance to try extreme points
you know, that's in programming.
You know, and there's a lot of this knowledge that's
very important.
And so it like this should all be part of the bundle
and it has a landscape and it has ways to navigate it.
And so that became my thesis and that my official thesis
advisor was Seymour Papert and all my committee
my co-advisor was Marvin Minsky and it was a lot of fun.
Cool.
What was the environment like at MIT at the time?
Who were you collaborating with?
Well, I wasn't collaborating with anybody on my thesis.
I mean, that was pretty much a solo enterprise.
The MIT style seems to be to set you loose
and give you all the freedom you want.
And for me that was great because I
knew what I wanted to do.
In retrospect, it would have been
better to have had more critical interactions
and perhaps more feedback on the goodness of the ideas.
But when I was in the, in the throes of it
I quite liked the freedom I had.
So I guess my contacts were some of the mathematicians,
some people like Ken Hoffman who's
an analyst, and people in the AI lab,
particularly Marvin and Seymour.
What was that atmosphere like at the AI lab then?
I don't know, pretty freewheeling, pretty tolerant
of all kinds of new ideas, very individualistic.
I think a lot of strong, smart individuals
you know who could sort of--
it wasn't a project oriented place say
as much as some place like Stanford
was which at least to me appeared to be more oriented
around projects that involved a lot of graduate students
working, there seem to be more solo enterprises.
And of course, I spent my time in a way in three
different entities at MIT.
There was the AI lab, there was the math department,
and there was another entity which no longer exists
called the Division For Study and Research
in Education which was trying to marry lots of disciplines
such as philosophy, linguistics, and AI
and mathematics that had along the perspective of education
and--
but it was broader than just teaching other people.
It was more of an AI cognitive science
kind of orientation and understanding
how thought goes in these other disciplines.
So there were people from [INAUDIBLE]
from the architecture school and there
was Jeanne Bamberger from music and it
was-- that was a very interesting place.
It was a cool place.
It was too bad that the enterprise didn't continue.
So speaking of those kinds of things what kind
of collaborations do you think exist between different fields
and Artificial Intelligence?
Or where potential is for that to happen?
Well, I think some non-computer science disciplines
have more intimate collaborations
with AI than others.
So for instance I think things like biology, neuroscience,
chemistry, VLSI, CAD/CAM engineering,
things like that have had quite an intimate relationship
with AI and that use the AI techniques.
Other disciplines are more at a distance
such as law and maybe ethics and some of the softer disciplines.
But I think the interaction there is just as interesting.
So in fact, after I had done this work in mathematics
I found out that most of my colleagues
were math-a-phobic or really didn't want to get
into it to the degree I did.
And I felt that the phenomenon of examples
and how you reason with them and how
you build them should be present in other disciplines.
So for instance, the way I wrote programs.
Very often if I had to write a new search program I
would look up the last one I wrote and tweak it
a bit, which is essentially a case based for example based
method of retrieving a good enough example
and then pushing it further.
That's also the way you can go counterexample.
So I tried to look at other disciplines besides mathematics
where the sort of problem solving behavior was useful.
And the one I--
it's useful in linguistics, but the one
I really love and have since spent most of my career in
is the legal domain because at least Anglo-American law
and other types of law as well but to a lesser extent
are based on cases.
In other words examples.
And what's interesting about cases and examples
in legal domains is there's a difference between real cases
and there's no hard definition of that and essentially
something that has been brought to the legal system
and hypotheticals.
Hypotheticals are made up cases, very good [INAUDIBLE]
experiments streamline kind of examples.
And they're more like counterexamples
in the sense of mathematics.
And they get used in Socratic dialogue,
the back and forth when you're trying
to test your ideas or the professors trying to push you
around to see whether what you're talking about,
or an oral argument in appellate situations
where someone's trying to see will your arguments stand up
if the situation is slightly different.
So I think there's a nice potential relationship
with disciplines like the law and a few of us
have pursued it.
But it's not as widespread as I'd
like because I think it's a rich domain.
So what can AI do for the field of law?
Well I think AI can do for--
What kind of applications are there in the near future?
Well, that's a different question
or that's a sub-question.
I think in the general sense AI can
do for law what it can do for many areas in that it gives
these other people in these other areas
who want to think about their field
a framework for thinking about their field.
So if you're interested in describing problem solving
or what it means to think like a lawyer
and to paraphrase Kingsfeld from the pictures.
The people in legal philosophy or who
look at legal reasoning on the legal side I don't think
have a very rich vocabulary for describing what it
means to think like a lawyer.
And for instance the doctrine of precedent
says that similar cases should be decided similarly
but there's no further explanation
of what it means for two cases to be similar.
Well in fact, there's lots of ways to describe similarities.
And AI gives you a handle on making quite precise what sort
of similarity you're interested in and how to manipulate it
and how to change it.
And so AI studies of legal reasoning
can shed light on the jurisprudence of a case based
reasoning and what it means for cases to be similar
and how do you create those nasty hypotheticals anyway?
And that not only is kind of a philosophical jurisprudential
enterprise, but it also could be an educational enterprise
if you want to teach law students how to push back
with good hypotheticals or you want people in practice
to think about how can you imagine situations that
might get you into trouble?
You need to think about hypotheticals, well
how should you come up with those kinds of cases?
So I think the kind of work that we do in AI demystifies
some of those processes which seem quite magical.
I mean, if you think back to your undergraduate years
taking real analysis how did those people come up
with those wild counterexamples?
It seemed like magic.
Well it's not magic, at least it's not all magic.
And the there are some models for how
to do that that can shed light on it
and those are AI's contribution, not only to the field
but to how to teach the field.
Well, so in the future do you see machines or computers
as being able to give advice to lawyers
or politicians like in a courtroom
or in a government setting?
I see them as able assistants.
I don't ever see them in the role of Judge.
I think that's silly because the law is
much more complex than subtle.
Right.
It's a very subtle enterprise.
But I think they certainly could help you to perfect
or at least uncover some of the obvious pitfalls
in your arguments.
They might help a beginning lawyer or someone
who's not an expert in an area operate with higher expertise.
I don't think they're necessarily
going to form the world's expert on the topic of their expertise
but I think they will enable.
I think they can be helpful.
That's a very exciting potential.
But they're-- but they're not going--
I don't-- my goal has not been to replace the lawyer
and it's certainly not to replace the judge.
I mean, I don't even consider that an interesting question.
But--
Cool.
So are there-- just to-- are there any
closing stories or anecdotes that you'd
like the tell about your experiences [INAUDIBLE]??
Well, maybe this comment that--
so going back to my interesting cases
and going back to the interconnections of knowledge,
in fact that was influenced a lot by my undergraduate
experience being in the computer science department where
hypertext was being developed.
And so I mean, that's an influence
on my way of thinking that I guess
is pretty pervasive but it comes from something
outside of mathematics which was my supposedly home
discipline, outside of AI which is my practicing discipline.
So I think maybe one of the lessons learned is you know,
it's a multifaceted enterprise and you
get a lot of information, a lot of ideas from elsewhere.
And it's a lot of fun.
It's a lot of fun to learn new things such as a new discipline
like the law.
There's lots of stories but you know.
Yeah.
So.
Great.
Well thank you so much.
It was my pleasure.
OK.